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Fact-checking & verificationPlatforms, policy & society
Fighting Misperceptions and Doubting Journalists' Objectivity: A Review of Fact-checking LiteratureComprehensive literature review of 48 studies on political fact-checking, organized into three research areas: (1) fact-checking effectiveness for correcting misperceptions (mixed results, including backfire effects); (2) fact-checking as a profession (contested methods and reliability); (3) public opinion about fact-checking (mostly…
Spread & networksPlatforms, policy & society
Social Media for Political Campaigns: An Examination of Trump's and Clinton's Frame Building and Its Effect on Audience EngagementThis study analyzes how Trump and Clinton's 2016 campaigns framed messages on Twitter and Facebook, and how frame type affected audience engagement. Trump relied on conflict and negative emotional frames, while Clinton used morality and positive emotional frames.
Detection methodsSpread & networks
dEFEND: Explainable Fake News DetectionFake news detection on social media typically focuses on content alone, but user comments and reactions provide complementary signals. This paper proposes dEFEND, a hierarchical attention neural network that jointly encodes news sentences and user comments to both detect fake news and explain why a piece of news is detected as fake by…
Spread & networksDetection methods
Fake News: Fundamental Theories, Detection Strategies and ChallengesA comprehensive tutorial framework addressing fake news research across three dimensions: (1) fundamental theories from psychology, social science, economics, and forensics explaining human vulnerability to misinformation and why individuals participate in fake news activities; (2) detection strategies unified across four perspectives…
Psychology & interventionsPlatforms, policy & society
Fake news game confers psychological resistance against online misinformationThis large-scale empirical study demonstrates that a free ~15-minute online browser game teaching six common misinformation production techniques (polarisation, emotional manipulation, conspiracy theories, trolling, discrediting opponents, impersonation) significantly reduces perceived reliability of misinformation across N=15,000…
Detection methodsDatasets & evaluation
Multi-Source Multi-Class Fake News DetectionProposes MMFD, a multi-source multi-class fake news detection framework combining automated feature extraction (CNN-LSTM), interpretable multi-source fusion (attention mechanism), and fakeness discrimination. Achieves 38.81% accuracy on LIAR dataset across six classes of fakeness (true, mostly-true, half-true, barely-true, false,…
Detection methodsLLMs & generative AI
Automated identification of media bias in news articles: an interdisciplinary literature reviewThis paper surveys automated and manual methods for identifying media bias in news articles, bridging social science and computer science approaches. It defines nine distinct forms of media bias and maps social science research onto computational techniques, finding that NLP-based methods are promising for automated detection but remain…
Psychology & interventionsPlatforms, policy & society
Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social MediaThis paper presents the first large-scale linguistic and psycholinguistic analysis of hate speech categorized by its target: directed (personal attacks) versus generalized (targeting groups). The authors analyze 28,318 directed and ~2,500 generalized hate tweets, revealing that directed hate is more informal, angry, and explicitly…
Spread & networksDetection methods
Studying Fake News via Network Analysis: Detection and MitigationThis chapter surveys network-based approaches to fake news detection and mitigation on social media, organizing fake news dissemination along three dimensions (content, social, temporal) and introduces methods exploiting network structure: homogeneous networks (friendship, diffusion, credibility) and heterogeneous networks (knowledge,…
Fact-checking & verificationDatasets & evaluation
The Fact Extraction and VERification (FEVER) Shared TaskThis paper presents results of the first FEVER shared task competition, which challenged 23 teams to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. The best system achieved 64.21% FEVER score, demonstrating the difficulty of joint evidence retrieval and natural…
Spread & networksBots & coordinated campaigns
On the Origins of Memes by Means of Fringe Web CommunitiesLarge-scale empirical study of how image memes originate and propagate across fringe and mainstream Web communities. Analyzes 160M images posted to Twitter, Reddit, /pol/, and Gab (2016–2017) using perceptual hashing, clustering, and custom distance metrics.
Detection methodsDatasets & evaluation
Detection and Resolution of Rumours in Social Media: A SurveyComprehensive survey of rumour detection and resolution in social media covering the entire pipeline: rumour definition and characterization, data collection strategies, annotation schemes, detection approaches for both emerging and long-standing rumours, tracking of rumour propagation, stance classification (support/deny/query/comment),…
Platforms, policy & societyFact-checking & verification
The Science of Fake NewsThis multidisciplinary review establishes a scientific framework for studying fake news, defining it as fabricated information that mimics news media form but lacks editorial norms. The paper identifies two mitigation strategies—empowering individuals through fact-checking and education, and structural platform interventions—while…
Spread & networksDetection methods
Rumor Detection on Twitter with Tree-structured Recursive Neural NetworksProposes two variants of recursive neural networks (RvNN)—bottom-up and top-down—that operate on propagation tree structures of tweets to detect rumors. Rather than treating tweets sequentially, the model learns representations that respect how tweets actually propagate in response threads.
Detection methodsDatasets & evaluation
Combating Fake News: A Survey on Identification and Mitigation TechniquesThis comprehensive survey of fake news detection and mitigation organizes existing methods into two broad categories: content-based approaches (linguistic and deep learning methods) and feedback-based approaches (exploiting propagation patterns, user responses, and temporal dynamics). The authors consolidate 23+ publicly available…
Spread & networksPlatforms, policy & society
Exposure to opposing views on social media can increase political polarizationThis field experiment tested whether exposure to opposing political views on social media reduces polarization. Twitter users (Republicans and Democrats) were randomly offered money to follow bots retweeting messages from opposing politicians and opinion leaders for one month.
Spread & networksPsychology & interventions
The small, disloyal fake news audience: The role of audience availability in fake news consumptionContrary to claims that fake news has reached a broad audience, this empirical analysis of comScore data shows the fake news audience is small and comprises heavy internet users. Applying the Law of Double Jeopardy from media theory, the authors show that fake news attracts disloyal audiences disproportionately from social platforms.
Spread & networksBots & coordinated campaigns
Influencers in Polarized Political Networks on TwitterThis paper identifies three types of influencers in polarized political Twitter conversations during Brazil's 2016 Dilma Rousseff impeachment: "opinion leaders" who shape discourse within ideological groups, "informational influencers" (media/institutions) drawn into one camp, and "activists" who amplify like-minded messages. The…
Bots & coordinated campaignsSpread & networks
Bots increase exposure to negative and inflammatory content in online social systemsSocial bots during the 2017 Catalan independence referendum strategically targeted influential humans to amplify negative and inflammatory content. Operating from network peripheries, bots coordinated sentiment-driven amplification that exacerbated polarization, with approximately one in three referendum-related tweets generated by bots.
Psychology & interventionsPlatforms, policy & society
Belief in conspiracy theories: Basic principles of an emerging research domainThis special issue introduction proposes four foundational principles for the emerging scientific study of conspiracy belief: conspiracy beliefs are consequential (with real impacts on health and relationships), universal (across cultures and historical periods), emotional (driven by sense-making rather than logic), and social (rooted in…
Deepfakes & multimodalDetection methods
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News DetectionThe paper proposes EANN, an adversarial neural network that detects fake news on newly emerged events by learning event-invariant features. An event discriminator forces the multi-modal feature extractor to remove event-specific features while preserving shared patterns across events.
Spread & networksDetection methods
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real NewsFake news differs systematically from real news in stylistic features, especially titles. Contrary to the assumption that fake news mimics real news, it is actually more similar to satire: packed titles with fewer stop words and more proper nouns, combined with shorter, repetitive body text with simpler language.
Detection methodsSpread & networks
"News you don't believe": Audience perspectives on fake newsUsing focus groups and surveys across the US, UK, Spain, and Finland, Nielsen and Graves find that ordinary people see fake news as a spectrum rather than a binary distinction—encompassing poor journalism, political propaganda, misleading advertising, and fabricated content. Audiences experience the "fake news" problem as one of degree,…
Detection methodsLLMs & generative AI
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on TwitterThis paper presents neural network models combining tweet content, social network interactions, and linguistic features (bias markers, subjectivity cues, moral foundations) to classify Twitter news posts as suspicious (propaganda, hoaxes, clickbait, satire) or verified. The models achieve 95% accuracy on binary classification and 0.7…